High Throughput Modelling of Molecular Crystals Out of Equilibrium (ht-MATTER)

非平衡态分子晶体的高通量建模 (ht-MATTER)

• 批准号: EP/X033139/1
• 负责人: Matteo Salvalaglio
• 金额: $ 219.62万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX033139%2F1
• 关键词: Throughput; Modelling; Molecular; Crystals; Out

Molecular crystals (MCs) are found in many everyday products, from food to pharmaceuticals, hiding in plain sight characteristics that make them ideal materials for cutting-edge technological applications. Like construction bricks, MCs' building blocks can reversibly self-assemble into a plurality of structures, thus distinguishing the chemical properties of building blocks from the physical and mechanical material properties. These characteristics open up endless possibilities in material design, with applications in pharmaceutical manufacturing, separations, catalysis, and organic electronics. Tailoring the composition of the liquid phase in which MCs assemble holds the key to designing processes able to yield materials with desired properties. However, current approaches at MCs computational design are centred on predicting the thermodynamic stability of bulk phases. This paradigm had remained essentially unchanged since its inception more than two decades ago, leaving material and process design practices to empiricism. By neglecting the role of assembly kinetics, current computational crystal structure prediction methods cannot identify attainable MC structures and the ideal conditions (i.e. solvent, composition, temperature) to obtain them.With ht-MATTER, I will bridge this gap by developing an open, transparent and flexible molecular simulation platform that will deploy state-of-the advanced molecular simulation methods necessary to model the out-of-equilibrium processes that govern crystal precipitation from solution at the atomistic scale. ht-MATTER will catalyse a paradigm shift in computational materials design by providing the high-throughput framework necessary to identify: a) MC structures attainable at finite-temperature; b) kinetic bottlenecks associated with crystal nucleation and growth from solution; and c) their dependence on solvent choice and solute concentration.

分子晶体（MC）存在于从食品到药品的许多日常产品中，隐藏在显而易见的特性中，使其成为尖端技术应用的理想材料。与建筑砖一样，MC的构建块可以可逆地自组装成多种结构，从而将构建块的化学性质与物理和机械材料性质区分开来。这些特性为材料设计开辟了无限的可能性，可应用于制药、分离、催化和有机电子领域。定制MC组装的液相的组成是设计能够产生具有所需特性的材料的工艺的关键。然而，目前的方法在MC计算设计集中在预测体相的热力学稳定性。自二十多年前成立以来，这种范式基本上保持不变，将材料和工艺设计实践留给了建筑主义。由于忽略了组装动力学的作用，当前的计算晶体结构预测方法不能识别可获得的MC结构和理想条件（即溶剂，组成，温度），以获得它们。与ht-MATTER，我将弥合这一差距，开发一个开放的，透明和灵活的分子模拟平台，将部署国家的先进分子模拟方法，必要的模型外，在原子尺度上控制晶体从溶液中沉淀的平衡过程。ht-MATTER将通过提供必要的高通量框架来识别：a）在有限温度下可获得的MC结构; B）与晶体成核和溶液生长相关的动力学瓶颈;以及c）它们对溶剂选择和溶质浓度的依赖性，从而催化计算材料设计的范式转变。

项目成果

Estimating Free Energy Surfaces and their Convergence from multiple, independent static and history-dependent biased molecular-dynamics simulations with Mean Force Integration.

使用平均力积分，通过多个独立的静态和历史相关的有偏差的分子动力学模拟来估计自由能表面及其收敛性。

• DOI: 10.26434/chemrxiv-2024-83h5q
• 发表时间: 2024
• 作者: Bjola A

Understanding the effect of moderate concentration SDS on CO2 hydrates growth in the presence of THF

了解中等浓度 SDS 在 THF 存在下对 CO2 水合物生长的影响

• DOI: 10.26434/chemrxiv-2023-1gqxv
• 发表时间: 2023
• 作者: Cai X

Machine Learning Nucleation Collective Variables with Graph Neural Networks

具有图神经网络的机器学习成核集体变量

• DOI: 10.26434/chemrxiv-2023-l6jjd
• 发表时间: 2023
• 作者: Dietrich F

Dynamics and nano-rheology of interfacial water: general discussion.

界面水动力学和纳米流变学：一般讨论。

• DOI: 10.1039/d3fd90064a
• 发表时间: 2024
• 期刊: Faraday discussions
• 影响因子: 3.4
• 作者: Advincula XR

Understanding the effect of moderate concentration SDS on CO2 hydrates growth in the presence of THF.

• DOI: 10.1016/j.jcis.2023.11.136
• 发表时间: 2023-11
• 期刊: Journal of colloid and interface science
• 影响因子: 9.9
• 作者: Xinrui Cai;Joshua Worley;A. Phan;Matteo Salvalaglio;Carolyn Koh;A. Striolo